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<b>请注意:</b><br>本书基于 Elasticsearch 2.x 版本，有些内容可能已经过时。
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<div class="chapter">
<div class="titlepage"><div><div>
<h2 class="title">
<a id="_looking_at_time"></a>按时间统计<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/edit/cn/300_Aggregations/35_date_histogram.asciidoc">edit</a>
</h2>
</div></div></div>
<p>如果搜索是在 Elasticsearch 中使用频率最高的，那么构建按时间统计的 date_histogram 紧随其后。  为什么你会想用 date_histogram 呢？</p>
<p>假设你的数据带时间戳。  无论是什么数据（Apache
事件日志、股票买卖交易时间、棒球运动时间）只要带有时间戳都可以进行 date_histogram 分析。当你的数据有时间戳，你总是想在 <em>时间</em> 维度上构建指标分析：</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
今年每月销售多少台汽车？
</li>
<li class="listitem">
这只股票最近 12 小时的价格是多少？
</li>
<li class="listitem">
我们网站上周每小时的平均响应延迟时间是多少？
</li>
</ul>
</div>
<p>虽然通常的 histogram 都是条形图，但 date_histogram 倾向于转换成线状图以展示时间序列。 许多公司用 Elasticsearch <em>仅仅</em> 只是为了分析时间序列数据。 <code class="literal">date_histogram</code> 分析是它们最基本的需要。</p>
<p><code class="literal">date_histogram</code> 与 通常的 <code class="literal">histogram</code> 类似。  但不是在代表数值范围的数值字段上构建 buckets，而是在时间范围上构建 buckets。 因此每一个 bucket 都被定义成一个特定的日期大小 (比如， <code class="literal">1个月</code> 或 <code class="literal">2.5 天</code> )。</p>
<div class="sidebar pagebreak-before">
<div class="titlepage"><div><div>
<p class="title"><strong>可以用通常的 histogram 进行时间分析吗？</strong></p>
</div></div></div>
<p>从技术上来讲，是可以的。  通常的 <code class="literal">histogram</code> bucket（桶）是可以处理日期的。 但是它不能自动识别日期。 而用 <code class="literal">date_histogram</code> ，你可以指定时间段如 <code class="literal">1 个月</code> ，它能聪明地知道 2 月的天数比 12 月少。
<code class="literal">date_histogram</code> 还具有另外一个优势，即能合理地处理时区，这可以使你用客户端的时区进行图标定制，而不是用服务器端时区。</p>
<p>通常的 histogram 会把日期看做是数字，这意味着你必须以微秒为单位指明时间间隔。另外聚合并不知道日历时间间隔，使得它对于日期而言几乎没什么用处。</p>
</div>
<p>我们的第一个例子将构建一个简单的折线图来回答如下问题：
每月销售多少台汽车？</p>
<div class="pre_wrapper lang-sense">
<pre class="programlisting prettyprint lang-sense">GET /cars/transactions/_search
{
   "size" : 0,
   "aggs": {
      "sales": {
         "date_histogram": {
            "field": "sold",
            "interval": "month", <a id="CO193-1"></a><i class="conum" data-value="1"></i>
            "format": "yyyy-MM-dd" <a id="CO193-2"></a><i class="conum" data-value="2"></i>
         }
      }
   }
}</pre>
</div>
<div class="sense_widget" data-snippet="snippets/300_Aggregations/35_date_histogram.json"></div>
<div class="calloutlist">
<table border="0" summary="Callout list">
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO193-1"><i class="conum" data-value="1"></i></a></p>
</td>
<td align="left" valign="top">
<p>时间间隔要求是日历术语 (如每个 bucket 1 个月)。</p>
</td>
</tr>
<tr>
<td align="left" valign="top" width="5%">
<p><a href="#CO193-2"><i class="conum" data-value="2"></i></a></p>
</td>
<td align="left" valign="top">
<p>我们提供日期格式以便 buckets 的键值便于阅读。</p>
</td>
</tr>
</table>
</div>
<p>我们的查询只有一个聚合，每月构建一个 bucket。这样我们可以得到每个月销售的汽车数量。 另外还提供了一个额外的 <code class="literal">format</code> 参数以便 buckets 有 "好看的" 键值。 然而在内部，日期仍然是被简单表示成数值。这可能会使得 UI 设计者抱怨，因此可以提供常用的日期格式进行格式化以更方便阅读。</p>
<p>结果既符合预期又有一点出人意料（看看你是否能找到意外之处）：</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">{
   ...
   "aggregations": {
      "sales": {
         "buckets": [
            {
               "key_as_string": "2014-01-01",
               "key": 1388534400000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-02-01",
               "key": 1391212800000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-05-01",
               "key": 1398902400000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-07-01",
               "key": 1404172800000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-08-01",
               "key": 1406851200000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-10-01",
               "key": 1412121600000,
               "doc_count": 1
            },
            {
               "key_as_string": "2014-11-01",
               "key": 1414800000000,
               "doc_count": 2
            }
         ]
...
}</pre>
</div>
<p>聚合结果已经完全展示了。正如你所见，我们有代表月份的 buckets，每个月的文档数目，以及美化后的 <code class="literal">key_as_string</code> 。</p>



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